Welcome on the homepage of the chair "Computer Graphics Systems" of Prof. Jürgen Döllner and his team. We like to inform you about our teaching and ongoing research activities in the analysis, planning and construction of computer graphics and multimedia systems.

Coherence-Enhancing Filtering

Directional features and flow-like structures are considered pleasant, harmonic, or at least interesting by most humans [Wei99]. They are also a highly sought-after property in many of the traditional art forms, such as paintings and illustrations. Enhancing directional coherence in the image helps to clarify region boundaries and features. As exemplified by Expressionism, it also helps to evoke mood or ideas and even elicit emotional response from the viewer. Particular examples include van Gogh and Munch, who have emphasized these features in their paintings. In this work, we present a new image and video abstraction technique that places emphasis on enhancing the directional coherence of features. The most notable related work in this category is image abstraction and stylization based on partial differential equations (PDE), in particular, shape-simplifying image abstraction by Kang and Lee [KL08] and Weickert’s coherence-enhancing shock filter [Wei03]. However, such PDE-based techniques may require a large number of iterations and tend to be unstable when used for video processing [Par08].

We build upon the idea of combining diffusion with shock filtering for image abstraction, but our approach is, in a sense, contrary to that of [KL08], which our technique outperforms in terms of speed, temporal coherence and stability. Instead of simplifying the shape of the image features, we aim to preserve the shape by using a curvature preserving smoothing method that enhances coherence. More specifically, our approach performs smoothing, in the direction of the smallest change, and sharpening, in the orthogonal direction. Instead of modeling this process by a PDE and solving it, we use approximations that operate as local filters on a neighborhood of a pixel. Therefore, good abstraction results are already achieved in a few iterations. This makes it possible to process images and video at real-time rates on a GPU. It also results in a much more stable algorithm that enables temporallycoherent video processing. Compared to the conventional abstraction approaches [WOG06,OBBT07,KKD09], we provide a good balance between the enhancement of directional features and the smoothing of isotropic regions. Our technique preserves and enhances directional features better and creates stronger contrast, which helps to clarify boundaries and features. Furthermore, our approach facilitates easy control over the level of abstractions.